AIエージェント
coze-studio
A visual all-in-one platform for building, debugging, and deploying AI agents.
★ 21.7k スターApache-2.0
ガイドを見る →coze-loopは、coze-devが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: coze-dev/coze-loop）。GitHubスターは5,765、フォークは801で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月8日です。
A full-lifecycle platform for developing, debugging, evaluating and monitoring AI agents.
Identify what you need to improve — prompt quality, evaluation coverage, or visibility into failed runs.
Clone the repo and prepare the Docker Compose deployment under release/deployment.
Edit model_config.yaml with your model provider's API key and endpoint/model ID.
Bring the services up and open the web interface to develop prompts, run evaluations and inspect traces.
Write, debug, optimize and version prompts in a visual playground with real-time interactive testing and side-by-side comparison.
Automated multi-dimensional evaluation of prompts and agent outputs — accuracy, conciseness, compliance and more.
Records the full execution process from user input to AI output, including prompt parsing, model invocation and tool execution, capturing intermediate results.
Development, debugging, evaluation and monitoring live together instead of in separate tools.
Runs via Docker Compose for a quick local deployment or via a published Helm chart on Kubernetes.
LLM integration is provided by the CloudWeGo Eino framework team, with a Go backend.
Clone Coze Loop and enter the project directory.
git clone https://github.com/coze-dev/coze-loop.git
cd coze-loopEdit the Docker Compose model config with your provider's API key and model/endpoint ID.
# Edit release/deployment/docker-compose/conf/model_config.yaml — set api_key and modelBring Coze Loop up with the provided make target and open the UI.
make compose-up
# Then open http://localhost:8082Coze Loop is maintained by coze-dev, the team behind Coze, with the Eino framework providing LLM integration.
The open-source version is Apache-2.0 licensed and free to self-host; Coze also offers hosted products separately.
Yes — you configure a model provider (the examples use Volcengine Ark / BytePlus ModelArk) with your own API key in model_config.yaml.
Those focus mainly on tracing and evaluation; Coze Loop covers the full lifecycle including a prompt development playground and version management, in a self-hostable Go platform.
Clone the repo, set your model in model_config.yaml, run make compose-up, and open http://localhost:8082.
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LibreAIカタログに掲載されたAIエージェントプロジェクトの中で最もスターの多いもの — coze-loopが合わなかった場合に検討したい、現実的な候補リストです。
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